In road traffic management, time taken to travel road segments is determined, and the same is used for various purposes. One such purpose is prediction of time that may be taken to travel a segment at a future time point. Currently various techniques have been provided to determine time taken to travel one or more road segments. Some of the techniques relate to systems and methods using vehicles with GPS-based devices as probes, cellular triangulation based solutions, and near field communication devices in vehicles, among others.
In estimating travel times, number of samples of travel times which are available for a road segment could be insufficient to compute a statistically accurate estimate of quantities such as average travel time, and standard deviation, among others.
In an existing technique using near field communication devices, near field communication device sensors network is deployed in a city. To determine travel times between two points “A” and “B”, near field communication sensor-A and sensor-B which are deployed at points “A” and “B” are used. Each of the sensors detects vehicles that have a near field communication device in them. When a vehicle V passes by the vicinity of sensor-A, sensor-A communicates with the near field communication device in the vehicle V and detects the identity of the near field communication device in vehicle V and notes the time at which the vehicle V passes sensor-A. Subsequently, further down on the same road stretch, when the vehicle passes sensor-B, the sensor notes down the identity of the near field communication device in vehicle V and the time at which it passes B. Sensors A and B communicate this information to a central server. The central server then computes the travel time of vehicle V from A to B. If a sufficient number of vehicles are detected on the road stretch from A to B, then a statistically accurate estimate of quantities such as, average time to travel on road stretch from A to B and standard deviation in the travel time, among others, can be computed more accurately. However the sensors may not detect every detectable vehicle because, the wireless medium could be lossy, especially because near field communication mostly happens over unlicensed ISM band and, many near field communication devices like Bluetooth go through sleep and awake cycle in passive mode. Hence, there is always a probability that a near-field communication device is in sleep mode for the entire duration of proximity to a sensor. Therefore the number of vehicles commonly detected by two sensors on a road stretch could be insufficient to compute a statistically accurate estimate of quantities such as the average travel time, the standard deviation etc.
This section introduces aspects that may be helpful in facilitating a better understanding of the invention. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is in the prior art or what is not in the prior art.